Ordinary Mixed Life Insurance and Mortality-Linked Insurance Contracts

Similar documents
Hedging with Life and General Insurance Products

induced by the Solvency II project

Managing Systematic Mortality Risk in Life Annuities: An Application of Longevity Derivatives

Basic Concepts and Examples in Finance

Binomial model: numerical algorithm

SPDE and portfolio choice (joint work with M. Musiela) Princeton University. Thaleia Zariphopoulou The University of Texas at Austin

Stochastic Processes and Stochastic Calculus - 9 Complete and Incomplete Market Models

1. For a special whole life insurance on (x), payable at the moment of death:

Indifference fee rate 1

AMH4 - ADVANCED OPTION PRICING. Contents

VALUATION OF FLEXIBLE INSURANCE CONTRACTS

Risk Neutral Valuation

M5MF6. Advanced Methods in Derivatives Pricing

Interest rate models in continuous time

Parameter sensitivity of CIR process

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t

Optimal trading strategies under arbitrage

Valuation of derivative assets Lecture 6

Valuation of derivative assets Lecture 8

1 Cash-flows, discounting, interest rates and yields

A. 11 B. 15 C. 19 D. 23 E. 27. Solution. Let us write s for the policy year. Then the mortality rate during year s is q 30+s 1.

KØBENHAVNS UNIVERSITET (Blok 2, 2011/2012) Naturvidenskabelig kandidateksamen Continuous time finance (FinKont) TIME ALLOWED : 3 hours

OPTIMAL PORTFOLIO CONTROL WITH TRADING STRATEGIES OF FINITE

Investigation of Dependency between Short Rate and Transition Rate on Pension Buy-outs. Arık, A. 1 Yolcu-Okur, Y. 2 Uğur Ö. 2

Longevity risk: past, present and future

MATH/STAT 4720, Life Contingencies II Fall 2015 Toby Kenney

1. The force of mortality at age x is given by 10 µ(x) = 103 x, 0 x < 103. Compute E(T(81) 2 ]. a. 7. b. 22. c. 23. d. 20

Chapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets

Optimal stopping problems for a Brownian motion with a disorder on a finite interval

Limit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies

STOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL

Equivalence between Semimartingales and Itô Processes

8.5 Numerical Evaluation of Probabilities

Asymmetric information in trading against disorderly liquidation of a large position.

STEX s valuation analysis, version 0.0

Asset Pricing Models with Underlying Time-varying Lévy Processes

Introduction Credit risk

Rough volatility models: When population processes become a new tool for trading and risk management

Constructing Markov models for barrier options

Hedging Credit Derivatives in Intensity Based Models

LIBOR models, multi-curve extensions, and the pricing of callable structured derivatives

Annuities. Lecture: Weeks 8-9. Lecture: Weeks 8-9 (Math 3630) Annuities Fall Valdez 1 / 41

Credit Risk Models with Filtered Market Information

Enlargement of filtration

Numerical schemes for SDEs

MATH 3630 Actuarial Mathematics I Class Test 2 - Section 1/2 Wednesday, 14 November 2012, 8:30-9:30 PM Time Allowed: 1 hour Total Marks: 100 points

Risk Neutral Measures

Credit Risk using Time Changed Brownian Motions

1.1 Basic Financial Derivatives: Forward Contracts and Options

Parameters Estimation in Stochastic Process Model

MATH3075/3975 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS

November 2012 Course MLC Examination, Problem No. 1 For two lives, (80) and (90), with independent future lifetimes, you are given: k p 80+k

Exact Sampling of Jump-Diffusion Processes

TEST OF BOUNDED LOG-NORMAL PROCESS FOR OPTIONS PRICING

A Proper Derivation of the 7 Most Important Equations for Your Retirement

Hedging of swaptions in a Lévy driven Heath-Jarrow-Morton framework

An overview of some financial models using BSDE with enlarged filtrations

The Forward PDE for American Puts in the Dupire Model

Survival models. F x (t) = Pr[T x t].

THE MARTINGALE METHOD DEMYSTIFIED

Pricing in markets modeled by general processes with independent increments

1 Consumption and saving under uncertainty

Sensitivity of American Option Prices with Different Strikes, Maturities and Volatilities

May 2012 Course MLC Examination, Problem No. 1 For a 2-year select and ultimate mortality model, you are given:

Option Pricing under Delay Geometric Brownian Motion with Regime Switching

EMPIRICAL EVIDENCE ON ARBITRAGE BY CHANGING THE STOCK EXCHANGE

Functional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs.

Tangent Lévy Models. Sergey Nadtochiy (joint work with René Carmona) Oxford-Man Institute of Quantitative Finance University of Oxford.

An Introduction to Point Processes. from a. Martingale Point of View

Local Volatility Dynamic Models

Exponential utility maximization under partial information

Consistently modeling unisex mortality rates. Dr. Peter Hieber, Longevity 14, University of Ulm, Germany

DEFERRED ANNUITY CONTRACTS UNDER STOCHASTIC MORTALITY AND INTEREST RATES: PRICING AND MODEL RISK ASSESSMENT

Conditional Density Method in the Computation of the Delta with Application to Power Market

The Black-Scholes Model

Deterministic Income under a Stochastic Interest Rate

The Capital Asset Pricing Model as a corollary of the Black Scholes model

MASM006 UNIVERSITY OF EXETER SCHOOL OF ENGINEERING, COMPUTER SCIENCE AND MATHEMATICS MATHEMATICAL SCIENCES FINANCIAL MATHEMATICS.

Risk Neutral Pricing. to government bonds (provided that the government is reliable).

INSTITUTE AND FACULTY OF ACTUARIES. Curriculum 2019 SPECIMEN SOLUTIONS

Optimal Search for Parameters in Monte Carlo Simulation for Derivative Pricing

Portability, salary and asset price risk: a continuous-time expected utility comparison of DB and DC pension plans

Drunken Birds, Brownian Motion, and Other Random Fun

3.4 Copula approach for modeling default dependency. Two aspects of modeling the default times of several obligors

Martingale Pricing Theory in Discrete-Time and Discrete-Space Models

PAPER 211 ADVANCED FINANCIAL MODELS

Operational Risk. Robert Jarrow. September 2006

Errata and Updates for ASM Exam MLC (Fifteenth Edition Third Printing) Sorted by Date

Stochastic Calculus, Application of Real Analysis in Finance

Stochastic Partial Differential Equations and Portfolio Choice. Crete, May Thaleia Zariphopoulou

How to hedge Asian options in fractional Black-Scholes model

Multiple Life Models. Lecture: Weeks Lecture: Weeks 9-10 (STT 456) Multiple Life Models Spring Valdez 1 / 38

Last Time. Martingale inequalities Martingale convergence theorem Uniformly integrable martingales. Today s lecture: Sections 4.4.1, 5.

CHAPTER 2: STANDARD PRICING RESULTS UNDER DETERMINISTIC AND STOCHASTIC INTEREST RATES

TWO-STAGE NEWSBOY MODEL WITH BACKORDERS AND INITIAL INVENTORY

Continuous Time Finance. Tomas Björk

Unified Credit-Equity Modeling

Queens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane.

PAPER 27 STOCHASTIC CALCULUS AND APPLICATIONS

Portfolio Optimization using Conditional Sharpe Ratio

Transcription:

Ordinary Mixed Life Insurance and Mortality-Linked Insurance Contracts M.Sghairi M.Kouki February 16, 2007 Abstract Ordinary mixed life insurance is a mix between temporary deathinsurance and pure endowment. Its duration is at least T years or more and in case of death the capital/pension will be paid to the inheritors and if the insured are alive, they will receive the payment. Thus, a single premium is paid by the policy-holders and placed by the insurance company on the incomplete financial market; but the difficulty lies in a fair tariffing. Our method is based primarily on two principles: equivalent martingale measure and the equality between the retrospective and the prospective market reserves. Then we obtain a condition on the discounting of the investment rate. This result generalizes the results obtained by M.Dahl [1] for a life insurance contract, in case of survival. Keywords: Life insurance; Stochastic mortality intensity; Incomplete financial markets; Lévy process; Equivalent measure principle 1 Introduction Traditionally, actuaries calculate premiums and market reserves using a deterministic mortality intensity that depends on age only, and a constant interest rate. But, in reality, neither the interest rate nor the mortality intensity are deterministic. It s well known, that we deal with two deferents life insurance contract : pure endowment and temporary death-insurance contracts. In this paper we address the case of an ordinary mixed insurance contract for only one x-year-old policy-holder, with stochastic mortality intensity, and an investment on the incomplete financial market. The ordinary 1

mixed insurance contract is a mix between temporary death insurance and pure endowment: - upon the policy-holder s death, a capital C0, 1 is paid to a designated recipient in the form of l benefits, in the event of death before the expiration date T of the contract, - a capital KT 2 is paid in full to the ensured person in the event of life at the expiry date (T) of the contract. This contract is paid by a single premium Π l 0 at time 0. In this paper we address the problem of fair pricing, which will be solved in two stages. First, by changing the initial probability measure in an equivalent measure, and then by expecting the retrospective and prospective market reserves. Thus, we obtain a necessary condition to the equity contract that relies on the discounting of the accumulation rate. In section 2 we present related to the financial market and those related the stochastic mortality. In section 3, we solve the problem by equalizing the time T retrospective and prospective market reserves. In section 4 we provide same Monte Carlo simulation experience, and section 5 concludes. 2 Assumptions and notations 2.1 The financial market We work on a space of probability (Ω, F,P) where filtration F = (F t ) t describes the information available on the incomplete financial market. We consider a financial market consisting of two traded assets : a risky asset with price process U and a locally risk-free asset with price process B (see T.Chan [4]). The stochastic process (U t ) t describes the accumulation rate. The dynamics of U and B may be given, for example, by : du t = α U (t,u t ) U t dt + σ U (t,u t ) U t dy t, > 0 db t = r t B t dt, B 0 = 1 (2.1) where r t 0 is the risk-free interest rate, α U (t,u t ) describes the expected behavior of the asset s instantaneous price, σ U (t,u t ) is uniformly bounded away from 0. It describes the sensitivity of the instantaneous price of the assets compared to the general trends in the 2

M.Sghairi, M.Kouki financial market. and (Y t ) t, a Lévy process on [0,T], is the index which describes the general trends in the financial market. For analytical purpose, we introduce the probability measure Q, equivalent to P, under which the discounted price process of the asset is a Q-martingale. Indeed, the expected discounted value is given by : p(t,t) = E Q [e R T t r udu F t ] so the financial annuity (see R.Cobbaut [3]) of an unity, paid in l periods, is given by l 1 a l = p(0,u). 2.2 The mortality u=0 The mortality is the second random component of the development of the ordinary mixed life insurance contract. It is linked to an F-adapted rightcontinuous Markov process Z = (Z t ) t [0,T], on a finite state space J = {0, 1} which describes the state of the insured person : 0 = alive, 1 = dead (see M.Dahl [1]). Only the transition from state 0 to state 1 is possible. On the one hand, under the initial probability P, the intensity of transition of Z is simply given at time t by the mortality intensity µ [x]+t, where, (µ [x]+t ) t is an adapted stochastic process. On the other hand, under the probability Q, the intensity of transition is multiplied by (1 + g t ), where (g t ) t is an adapted process, strictly higher than 1 Q-almost surely. Then we introduce the survival probability of an age [x]+t person from time t to T. It is given by G Q (t,µ [x]+t,t) = E Q [e R T t (1+gu)µ [x]+udu µ [x]+t ] where E Q indicates the expectation under Q. Then the death probability estimated at time t under Q is given by Ḡ Q (t,µ [x]+t,t) = 1 G Q (t,µ [x]+t,t). We denote by (K 2 t ) t T the adapted stochastic process, equal to the ensured sum at time t for pure endowment in the event of survival. In particular K 2 T indicates the sum to be paid at time T in the survival event at age x + T. 3

3 Solving the problem The equivalent measure Q principle yields the premium Π l 0 for the ordinary mixed life insurance contract, with sum C0 1 as the ensured sum in case of death, and KT 2 as the ensured sum in case of life. Let us notice that on model is an extension of the Norberg [2] model. Thus, the expected discounted value under Q of the actual payments having to be of null Q-mean, we have T Π l 0 = K0p(0,T)G 2 Q (0,µ [x],t) C1 0 l a l p(0,s) s G Q (0,µ [x],s)ds. (3.1) 0 Note that K 1,l 0 = C1 0 l a l the actual annuity value in l terms and the instantaneous risk of death is s G Q (0,µ [x],s). Proposition 3.1: Let the actual payments Π l 0 by given by (3.1) Then E Q [exp( T r 0 udu) U T ] = 1 + K1,l 0 [p(0,t)ḡq (0,µ [x],t) Π l 0 + T 0 (1 + g s)µ [x]+s p(0,s)g Q (0,µ [x],s)ds] (3.2) Proof : Let V i,l,retro t be the retrospective reserves conditioned at time t [0, T], given that the policy-holder is in the Z t = i state, is defined by: V i,l,retro t = Π l 0 U t K 1,l 0 1 {i=1} 1 {t<t } K 2 T 1 {i=0} 1 {t=t } (3.3) where K 1,l 0 = C1 0 l a l indicates the actual annuity value in l terms and i is person s state at time t. Let V i,l,pro t be the prospective reserves at time t, given that the policy-holder is in the Z t = i state. The sum insured in the event of life is Kt 2 and we consider that the sum insured in the event of death is constant in a course 4

M.Sghairi, M.Kouki of time equal to K 1,l 0. Therefore V i,l,pro t = E Q [K 1,l T 0 (1 + g t s)µ [x]+s e R s t ru+(1+gu)µ [x]+udu ds + e R T t r udu e R T 0 (1+gu)µ[x]+udu Kt 2 Z t = i, I t G t ], 0 t < T (3.4) To determine the adapted advantage, Kt 2, we use the following criterion: E Q [V Zt,l,pro t I t G t ] = E Q [V Zt,l,retro t I t G t ] (3.5) It is to be noted that in the above equality, the expectation relates only to the values of Z t. For the contract to be fair the expected discounted value under Q of the actual payments should be 0, i.e. E Q [Π l 0 + K 1,l 0 T 0 (1 + g s)µ [x]+s e R s t ru+(1+gu)µ [x]+udu ds K 2 T e R T 0 ru+(1+gu)µ [x]+udu ] = 0. (3.6) This leads to a condition on the process of accumulation U. Let us express K 2 T in terms of U, by the fact that the prospective reserve at time T must be null: V 0,l,pro T = V 1,l,pro T = 0. According to (3.3) we have : V 0,l,retro T = Π l U T 0 KT 2 K 1,l 0. Thus, criterion (3.5), applied at time T, gives : et V 1,l,retro T = Π l 0 U T KT 2 = Π l U T 1 0 G Q (0,µ [x],t) Ḡ Q (0,µ [x],t) K1,l 0 G Q (0,µ [x],t) with ḠQ (0,µ [x],t) = 1 G Q (0,µ [x],t) as the death probability from time 0 to time T. While inserting this in (3.6), one notes that the process U must check : E Q [e R T 0 rudu U T ] = 1 + K1,l 0 Π l [p(0,t)ḡq (0,µ [x],t) 0 + T 0 (1 + g s)µ [x]+s p(0,s)g Q (0,µ [x],s)ds]. (3.7) 5

In particular, it is to be noted that the expected discounted value of accumulation factor U T from 0 to T, E Q [exp( T r 0 udu) U T ], is higher than 1 whereas under the modeling of M.Dahl [1] it is equal to 1. After solving the problem of a fair contract we addressed the development of the benefits. Criterion (3.5) at time t < T is written as : Π l 0 Ut = e R t 0 (1+gu)µ [x]+udu K 2 t p(t,t)g Q (t,µ [x]+t,t) + K 1,l 0 T t (1 + g s)µ [x]+s p(0,s)g Q (0,µ [x],s)ds (3.8) Using the expression of premium (3.1), we express the advantages at time t according to the advantages at time 0 R t0 Kt 2 (1+gu)µ e [x]+u du = p(t,t)g Q (t,µ [x]+t,t) [K2 0p(0,T)G Q (0,µ [x],t) Ut + K 1,l 0 [ Ut T 0 (1 + g s)µ [x]+s p(0,s)g Q (0,µ [x],s)ds (3.9) T t (1 + g s)µ [x]+s p(0,s)g Q (0,µ [x],s)ds]. 4 Monte Carlo study In this section we simulate the loss risk. To get a realistic portfolio of the insured, it would be necessary to introduce policy-holders of different ages/cohorts, different horizons, and different dates of subscription. However, in this study, we started with simulations of homogeneous portfolios. We simulated the risk of loss of 1000 ordinary mixed life insurance contracts, concerning x-year insured people, x varying in somme interval I, and of fixed horizon T years. We used the mortality intensity of Gompertz-Makeham µ x = a + bc x of parameter a = 0.000233, b = 0.0000658 et c = 1.0959. Example: If the insured have different ages/cohorts, then the loss risk of the associated portfolio at T can be obtained as a weighted mean of the individuel risks. The left figure represent the dynamics of assets price is of the form du t = αu t dt + σu t dy t, where α = 0.6, and σ = 0.2, and Y t is a Lévy process of the 6

M.Sghairi, M.Kouki form dy t = dw t + dn t where W is the Brownian motion and N the Poisson process of 10% intensity, and that him of right-hand side the dynamics of assets price is of the form du t = αu t dt + σu t dw t. We calculated V Zt,x,retro t t [0,T], x I, given by formula (3.3) for 1000 contracts with C0 1 = 1, KT 2 = 0.5. We simulated the loser contract rate at time t [0,T] for N insured persons with ages x I using the approximation : t [0,T], x I : 1 N N i=1 1 {V x t <0} P(V x t < 0) Figure 1 : The left figure represent the risk of loss of contracts of 1000 insured persons, x [30, 60] years and horizon T = 30 years, with the dynamics of assets price is of the form du t = αu t dt + σu t dy t and that him of right-hand side, with the dynamics of assets price is of the form du t = αu t dt + σu t dw t. Figure 2 : The left figure represent the risk of loss of contracts of 1000 insured persons, x [40, 60] years and horizon T = 20 years, with the dynamics of assets price is of the form du t = αu t dt + σu t dy t and that him of right-hand side, with the dynamics of assets price is of the form du t = αu t dt + σu t dw t. Figure 3 :The left figure represent the risk of loss of contracts of 1000 insured persons, x [35, 55] years and horizon T = 20 years, with the dynamics of assets price is of the form du t = αu t dt + σu t dy t and that him of right-hand side, with the dynamics of assets price is of the form du t = αu t dt + σu t dw t. 7

Figure 1: Risk of loss Figure 2: Risk of loss 8

M.Sghairi, M.Kouki Figure 3: Risk of loss 5 Conclusion Our objective in this study was to find a tool to determine the premium that policy-holders have to pay to insurers according to the terms of the contracts. However the contracts mention the expiry date, as well as the mode of payment of the annuities due by the policy-holder and the two parties operate in a context of information asymmetry. In this context each individual tries to maximize his/her utility function, in other terms his/her future benefit. The uncertainty that prevails in all incomplete contracts requires an estimate of the risk induced by incomplete information. From this point of view we tried to design a mathematical model in order to help the insurer take his/her decision, estimate the risks, and determine the premium of the contract while taking into account unquestionable variables: mortality intensity rate, the characteristics of each insurance contract(temporary death-insurance, pure endowment). Certain assumptions were put forward to design a model that considers the determination of the premium as well as the risk. An empirical study was undertaken to highlight the reliability of our model. Indeed the model shows that in the event of non-diversification of the insurer s portfolios, the risk of loss caused varies according to the expiry date of the contract. However these results must not be considered as the limits of our model, since the data used to test this model were subjective data which do not reflect 9

the reality. Collecting data in this field remains complex and it is the major problem for the validation of our model. References [1] M. Dahl. Stochastic mortality in life insurance:market reserves and mortality-linked insurance contracts. Insurance:Mathematics and Economics 35., pages 113 136, 2004. [2] R. Norberg. Reserves in life and pension insurance. Scandinavian Actuarial Journal 1, pages 3 24, 1991. [3] R.Cobbaut. Théorie Financière. Economica, Paris, 1994. [4] T.Chan. Pricing contingent claims on stocks driven by lévy processes. The Annals of Applied Probability., pages 504 528, 1999, Vol.9. M.Sghairi M.Kouki Université PARIS DESCARTES Ecole Supérieure de la Statistique LABORATOIRE MAP5 et de l Analyse de l Information 45, RUE DES SAINTS-PÈRES 6, rue des mètiers 75270 PARIS CEDEX 6 2035 Charguia 2 Ariana FRANCE TUNISIE sghairi@math-info.univ-paris5.fr mokhtar.kouki@essai.rnu.tn 10